import numpy as np 
import pandas as pd 

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

data = pd.read_csv("../input/asian-restaurants/asia.csv")
data
case restaurant price gReviews gRating ddReviews ddRating ddFee yReviews yRating latitude longitude town
0 2 Sun Shui 2 324 4.5 0 0.0 0.00 154 4.0 42.919826 -71.461006 Bedford
1 4 Chen Yang Li B 2 468 4.2 0 0.0 0.00 83 3.0 43.170056 -71.535558 Bow
2 6 Dragon Star 2 126 4.2 0 0.0 0.00 28 3.5 43.200392 -71.532658 Concord
3 11 Siam Orchid Thai 2 417 4.2 0 0.0 0.00 80 3.5 43.204658 -71.535469 Concord
4 12 Splendid Sushi 2 135 4.9 0 0.0 0.00 8 4.5 43.204005 -71.536732 Concord
... ... ... ... ... ... ... ... ... ... ... ... ... ...
98 85 Wok N Roll 2 107 4.3 2900 4.6 0.99 27 4.0 42.753885 -71.462696 Nashua
99 61 Thousand Crane 2 176 3.5 3700 4.6 1.99 99 3.0 42.992559 -71.463695 Manchester
100 50 Kisaki Japanese 2 590 4.4 3800 4.7 1.99 112 4.0 42.987353 -71.462839 Manchester
101 41 Cheng Du Kitchen 2 31 4.5 5900 4.7 3.99 41 3.5 43.016959 -71.449888 Manchester
102 103 Jade Garden 2 96 4.2 0 0.0 0.00 26 4.0 43.048281 -71.434783 Hooksett

103 rows × 13 columns

I have separated the numerical columns from the whole dataframes 

numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
numeric_df = data.select_dtypes(include=numerics)
numeric_df
case price gReviews gRating ddReviews ddRating ddFee yReviews yRating latitude longitude
0 2 2 324 4.5 0 0.0 0.00 154 4.0 42.919826 -71.461006
1 4 2 468 4.2 0 0.0 0.00 83 3.0 43.170056 -71.535558
2 6 2 126 4.2 0 0.0 0.00 28 3.5 43.200392 -71.532658
3 11 2 417 4.2 0 0.0 0.00 80 3.5 43.204658 -71.535469
4 12 2 135 4.9 0 0.0 0.00 8 4.5 43.204005 -71.536732
... ... ... ... ... ... ... ... ... ... ... ...
98 85 2 107 4.3 2900 4.6 0.99 27 4.0 42.753885 -71.462696
99 61 2 176 3.5 3700 4.6 1.99 99 3.0 42.992559 -71.463695
100 50 2 590 4.4 3800 4.7 1.99 112 4.0 42.987353 -71.462839
101 41 2 31 4.5 5900 4.7 3.99 41 3.5 43.016959 -71.449888
102 103 2 96 4.2 0 0.0 0.00 26 4.0 43.048281 -71.434783

103 rows × 11 columns

The boxplots for all the numerical columns are plotted 

import matplotlib.pyplot as plt

for col in numeric_df.columns:
    plt.title('Boxplot of {}'.format(col))
    plt.boxplot(data[col])
    plt.show()

The initial distributions are plotted for all the numerical columns 

for col in numeric_df.columns:
    plt.title('Boxplot of {}'.format(col))
    plt.hist(data[col])
    plt.show()

Checking to see which town has the most ratings 

pivot = data.pivot_table(index=['town'], values=['yRating'], aggfunc='count')
print (pivot)

                yRating
town                   
Bedford               3
Bow                   1
Concord              10
Derry                 6
East Hampstead        1
Epsom                 1
Hampstead             1
Hooksett              2
Hudson                4
Londonderry           5
Manchester           31
Merrimack             5
Nashua               18
Pembroke              1
Penacook              1
Plaistow              2
Salem                 9
Windham               2

Basemap for plotting the map 

from mpl_toolkits.basemap import Basemap

plt.figure(figsize=(8, 8))
m = Basemap(projection='ortho', resolution=None, lat_0=1.35, lon_0=103)
m.bluemarble(scale=0.5);

Getting the unique dataframe of all the towns 

town_uni = data.drop_duplicates(subset = ["town"])
town_uni.reset_index(inplace=True)
town_uni
index case restaurant price gReviews gRating ddReviews ddRating ddFee yReviews yRating latitude longitude town
0 0 2 Sun Shui 2 324 4.5 0 0.0 0.0 154 4.0 42.919826 -71.461006 Bedford
1 1 4 Chen Yang Li B 2 468 4.2 0 0.0 0.0 83 3.0 43.170056 -71.535558 Bow
2 2 6 Dragon Star 2 126 4.2 0 0.0 0.0 28 3.5 43.200392 -71.532658 Concord
3 7 15 China Garden 1 144 3.9 0 0.0 0.0 74 3.5 42.898300 -71.335473 Derry
4 9 21 Golden Leaf 2 78 3.8 0 0.0 0.0 21 3.0 42.891259 -71.139307 East Hampstead
5 10 22 Happy Family 2 291 4.3 0 0.0 0.0 23 3.0 43.230217 -71.361415 Epsom
6 11 23 Osaka 2 192 4.0 0 0.0 0.0 50 3.0 42.887691 -71.149027 Hampstead
7 12 24 Asian Breeze 2 386 4.4 0 0.0 0.0 87 4.0 43.053215 -71.442233 Hooksett
8 13 25 Dynamite Sushi 2 249 4.4 0 0.0 0.0 150 4.0 42.759067 -71.430858 Hudson
9 14 30 Bangkok Thai Food 2 192 4.7 0 0.0 0.0 111 4.5 42.866402 -71.349207 Londonderry
10 17 34 Aloha Restaurant 2 481 4.2 0 0.0 0.0 85 3.0 42.986402 -71.426531 Manchester
11 33 67 Hana Japanese 3 82 4.8 0 0.0 0.0 39 4.5 42.837345 -71.499091 Merrimack
12 36 73 Lilac Blossom 2 591 4.3 0 0.0 0.0 103 3.5 42.707436 -71.467752 Nashua
13 41 88 Mei's 2 116 3.7 0 0.0 0.0 46 3.5 43.130859 -71.452656 Pembroke
14 42 89 Asian Taste 1 76 4.4 0 0.0 0.0 21 4.0 43.267225 -71.587472 Penacook
15 43 90 Chang Long 1 93 4.1 0 0.0 0.0 51 4.0 42.849543 -71.104977 Plaistow
16 45 92 Lucky Dragon 1 231 3.9 0 0.0 0.0 37 2.5 42.754452 -71.207800 Salem
17 50 102 Li's Asian Gourmet 2 40 4.2 0 0.0 0.0 25 2.5 42.792473 -71.304759 Windham

Install the full package

conda install -c conda-forge basemap-data-hires 

for i,j in enumerate(town_uni['town']):
    fig = plt.figure(figsize=(8, 8))
    m = Basemap(projection='lcc', resolution=None,
                width=8E6, height=8E6, 
                lat_0=town_uni['latitude'][i], lon_0=town_uni['longitude'][i])
    m.etopo(scale=0.5, alpha=0.5)

    x, y = m(town_uni['longitude'][i], town_uni['latitude'][i])
    plt.plot(x, y, 'ok', markersize=5)
    plt.text(x, y, town_uni['town'][i], fontsize=12);
    plt.show()

Plotting a zoomed map 

fig = plt.figure(figsize=(8, 8))
m = Basemap(projection='lcc', resolution=None,
            width=4E5, height=4E5, 
            lat_0=data['latitude'][0], lon_0=data['longitude'][0])
m.etopo(scale=7, alpha=0.5)

x, y = m(data['longitude'][0], data['latitude'][0])
plt.plot(x, y, 'ok', markersize=5)
plt.text(x, y, data['town'][0], fontsize=12);